The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Supervised deep learning methods require a large repository of annotated data; hence,
label noise is inevitable. Training with such noisy data negatively impacts the generalization …

We present a novel vision-language prompt learning approach for few-shot out-of-
distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from …

Cataracts are the leading cause of vision loss worldwide. Restoration algorithms are
developed to improve the readability of cataract fundus images in order to increase the …

Unsupervised domain adaptation for intelligent fault diagnosis requires a well-annotated
source domain to transfer knowledge to an unlabeled target domain, but the ubiquitous …

Graph Neural Networks (GNNs) have become widely-used models for semi-supervised
learning. However, the robustness of GNNs in the presence of label noise remains a largely …

Pneumonia is one of the most common treatable causes of death, and early diagnosis
allows for early intervention. Automated diagnosis of pneumonia can therefore improve …

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-
quality labeled data from the source domain. However, in engineering scenarios, achieving …

In recent years, skeleton-based action recognition has achieved remarkable performance in
understanding human motion from sequences of skeleton data, which is an important …

Cross-scene classification has recently gained increasing interest, which improves the
classification performance on label-scarce domains by transferring knowledge learned from …